





'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    for i in range(config.num_hidden_layers     ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')     )
        rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')     )
        rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')     )
        rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')     )
        rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')     )
        rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')     )
        rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')     )
        rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')     )
        rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')     )
        rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')     )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ("cls_token", "deit.embeddings.cls_token"),
            ("dist_token", "deit.embeddings.distillation_token"),
            ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
            ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
            ("pos_embed", "deit.embeddings.position_embeddings"),
        ]     )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("norm.weight", "layernorm.weight"),
                ("norm.bias", "layernorm.bias"),
                ("pre_logits.fc.weight", "pooler.dense.weight"),
                ("pre_logits.fc.bias", "pooler.dense.bias"),
            ]     )

        # if just the base model, we should remove "deit" from all keys that start with "deit"
        _lowerCAmelCase						      =   [(pair[0], pair[1][4:]) if pair[1].startswith("deit"     ) else pair for pair in rename_keys]
    else:
        # layernorm + classification heads
        rename_keys.extend(
            [
                ("norm.weight", "deit.layernorm.weight"),
                ("norm.bias", "deit.layernorm.bias"),
                ("head.weight", "cls_classifier.weight"),
                ("head.bias", "cls_classifier.bias"),
                ("head_dist.weight", "distillation_classifier.weight"),
                ("head_dist.bias", "distillation_classifier.bias"),
            ]     )

    return rename_keys


def 						__a(SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       int=False     ):



    '''simple docstring'''





    for i in range(config.num_hidden_layers     ):
        if base_model:
            _lowerCAmelCase						      =   ""
        else:
            _lowerCAmelCase						      =   "deit."
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _lowerCAmelCase						      =   state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''     )
        _lowerCAmelCase						      =   state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''     )
        # next, add query, keys and values (in that order) to the state dict
        _lowerCAmelCase						      =   in_proj_weight[
            : config.hidden_size, :
        ]
        _lowerCAmelCase						      =   in_proj_bias[: config.hidden_size]
        _lowerCAmelCase						      =   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _lowerCAmelCase						      =   in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _lowerCAmelCase						      =   in_proj_weight[
            -config.hidden_size :, :
        ]
        _lowerCAmelCase						      =   in_proj_bias[-config.hidden_size :]


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   dct.pop(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   val


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
    _lowerCAmelCase						      =   Image.open(requests.get(SCREAMING_SNAKE_CASE_		,							stream=SCREAMING_SNAKE_CASE_     ).raw     )
    return im


@torch.no_grad()
def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   DeiTConfig()
    # all deit models have fine-tuned heads
    _lowerCAmelCase						      =   False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    _lowerCAmelCase						      =   1000
    _lowerCAmelCase						      =   "huggingface/label-files"
    _lowerCAmelCase						      =   "imagenet-1k-id2label.json"
    _lowerCAmelCase						      =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							repo_type="dataset"     )		,							"r"     )     )
    _lowerCAmelCase						      =   {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
    _lowerCAmelCase						      =   idalabel
    _lowerCAmelCase						      =   {v: k for k, v in idalabel.items()}
    _lowerCAmelCase						      =   int(deit_name[-6:-4]     )
    _lowerCAmelCase						      =   int(deit_name[-3:]     )
    # size of the architecture
    if deit_name[9:].startswith("tiny"     ):
        _lowerCAmelCase						      =   192
        _lowerCAmelCase						      =   768
        _lowerCAmelCase						      =   12
        _lowerCAmelCase						      =   3
    elif deit_name[9:].startswith("small"     ):
        _lowerCAmelCase						      =   384
        _lowerCAmelCase						      =   1536
        _lowerCAmelCase						      =   12
        _lowerCAmelCase						      =   6
    if deit_name[9:].startswith("base"     ):
        pass
    elif deit_name[4:].startswith("large"     ):
        _lowerCAmelCase						      =   1024
        _lowerCAmelCase						      =   4096
        _lowerCAmelCase						      =   24
        _lowerCAmelCase						      =   16

    # load original model from timm
    _lowerCAmelCase						      =   timm.create_model(SCREAMING_SNAKE_CASE_		,							pretrained=SCREAMING_SNAKE_CASE_     )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    _lowerCAmelCase						      =   timm_model.state_dict()
    _lowerCAmelCase						      =   create_rename_keys(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    read_in_q_k_v(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    # load HuggingFace model
    _lowerCAmelCase						      =   DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_     ).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_     )

    # Check outputs on an image, prepared by DeiTImageProcessor
    _lowerCAmelCase						      =   int(
        (256 / 224) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    _lowerCAmelCase						      =   DeiTImageProcessor(size=SCREAMING_SNAKE_CASE_		,							crop_size=config.image_size     )
    _lowerCAmelCase						      =   image_processor(images=prepare_img()		,							return_tensors="pt"     )
    _lowerCAmelCase						      =   encoding["pixel_values"]
    _lowerCAmelCase						      =   model(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   timm_model(SCREAMING_SNAKE_CASE_     )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(SCREAMING_SNAKE_CASE_		,							outputs.logits		,							atol=1e-3     )

    Path(SCREAMING_SNAKE_CASE_     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
    print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''     )
    model.save_pretrained(SCREAMING_SNAKE_CASE_     )
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''     )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       _SCREAMING_SNAKE_CASE									=				parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{'''vocab_file''': '''spiece.model'''}

lowerCamelCase__  =					{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

lowerCamelCase__  =					{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
lowerCamelCase__  =					0
lowerCamelCase__  =					1
lowerCamelCase__  =					2
lowerCamelCase__  =					3
lowerCamelCase__  =					4




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					VOCAB_FILES_NAMES
 __A			       =					PRETRAINED_VOCAB_FILES_MAP
 __A			       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __A			       =					'''left'''







 def __init__(		self				:   int							, lowercase_				:   Optional[int]							, lowercase_				:   Dict=False							, lowercase_				:   Any=True							, lowercase_				:   Any=False							, lowercase_				:   Any="<s>"							, lowercase_				:   Dict="</s>"							, lowercase_				:   Optional[Any]="<unk>"							, lowercase_				:   Optional[int]="<sep>"							, lowercase_				:   Optional[Any]="<pad>"							, lowercase_				:   Tuple="<cls>"							, lowercase_				:   Union[str, Any]="<mask>"							, lowercase_				:   Dict=["<eop>", "<eod>"]							, lowercase_				:   Optional[Dict[str, Any]] = None							, **lowercase_				:   int							, )						->	None:


   """simple docstring"""







   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else mask_token

   _UpperCamelCase           =    {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=lowercase_							, remove_space=lowercase_							, keep_accents=lowercase_							, bos_token=lowercase_							, eos_token=lowercase_							, unk_token=lowercase_							, sep_token=lowercase_							, pad_token=lowercase_							, cls_token=lowercase_							, mask_token=lowercase_							, additional_special_tokens=lowercase_							, sp_model_kwargs=self.sp_model_kwargs							, **lowercase_							, )

   _UpperCamelCase           =    3

   _UpperCamelCase           =    do_lower_case
   _UpperCamelCase           =    remove_space
   _UpperCamelCase           =    keep_accents
   _UpperCamelCase           =    vocab_file

   _UpperCamelCase           =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	Dict:


   """simple docstring"""







   return len(self.sp_model)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    {self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab







 def __getstate__(		self				:   Optional[int])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.__dict__.copy()
   _UpperCamelCase           =    None
   return state







 def __setstate__(		self				:   Tuple							, lowercase_				:   List[str])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    d

   # for backward compatibility
   if not hasattr(self							, "sp_model_kwargs"):
     _UpperCamelCase           =    {}

   _UpperCamelCase           =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   if self.remove_space:
     _UpperCamelCase           =    " ".join(inputs.strip().split())
   else:
     _UpperCamelCase           =    inputs
   _UpperCamelCase           =    outputs.replace("``"							, "\"").replace("''"							, "\"")

   if not self.keep_accents:
     _UpperCamelCase           =    unicodedata.normalize("NFKD"							, lowercase_)
     _UpperCamelCase           =    "".join([c for c in outputs if not unicodedata.combining(lowercase_)])
   if self.do_lower_case:
     _UpperCamelCase           =    outputs.lower()

   return outputs







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   str)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    self.preprocess_text(lowercase_)
   _UpperCamelCase           =    self.sp_model.encode(lowercase_							, out_type=lowercase_)
   _UpperCamelCase           =    []
   for piece in pieces:
     if len(lowercase_) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
       _UpperCamelCase           =    self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_							, ""))
       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
         if len(cur_pieces[0]) == 1:
           _UpperCamelCase           =    cur_pieces[1:]
         else:
           _UpperCamelCase           =    cur_pieces[0][1:]
       cur_pieces.append(piece[-1])
       new_pieces.extend(lowercase_)
     else:
       new_pieces.append(lowercase_)

   return new_pieces







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Tuple)						->	List[str]:


   """simple docstring"""







   return self.sp_model.PieceToId(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   int)						->	Any:


   """simple docstring"""







   return self.sp_model.IdToPiece(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   Union[str, Any])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "".join(lowercase_).replace(lowercase_							, " ").strip()
   return out_string







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   List[int]							, lowercase_				:   bool = False							, lowercase_				:   bool = None							, lowercase_				:   bool = True							, **lowercase_				:   int							, )						->	str:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.pop("use_source_tokenizer"							, lowercase_)

   _UpperCamelCase           =    self.convert_ids_to_tokens(lowercase_							, skip_special_tokens=lowercase_)

   # To avoid mixing byte-level and unicode for byte-level BPT
   # we need to build string separately for added tokens and byte-level tokens
   # cf. https://github.com/huggingface/transformers/issues/1133
   _UpperCamelCase           =    []
   _UpperCamelCase           =    []
   for token in filtered_tokens:
     if skip_special_tokens and token in self.all_special_ids:
       continue
     if token in self.added_tokens_encoder:
       if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(lowercase_))
         _UpperCamelCase           =    []
       sub_texts.append(lowercase_)
     else:
       current_sub_text.append(lowercase_)
   if current_sub_text:
     sub_texts.append(self.convert_tokens_to_string(lowercase_))

   # Mimic the behavior of the Rust tokenizer:
   # By default, there are no spaces between special tokens
   _UpperCamelCase           =    "".join(lowercase_)

   _UpperCamelCase           =    (
       clean_up_tokenization_spaces
       if clean_up_tokenization_spaces is not None
       else self.clean_up_tokenization_spaces
   )
   if clean_up_tokenization_spaces:
     _UpperCamelCase           =    self.clean_up_tokenization(lowercase_)
     return clean_text
   else:
     return text







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [self.cls_token_id]
   if token_ids_a is None:
     return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None							, lowercase_				:   bool = False)						->	List[int]:


   """simple docstring"""







   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowercase_							, token_ids_a=lowercase_							, already_has_special_tokens=lowercase_)

   if token_ids_a is not None:
     return ([0] * len(lowercase_)) + [1] + ([0] * len(lowercase_)) + [1, 1]
   return ([0] * len(lowercase_)) + [1, 1]







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [2]

   if token_ids_a is None:
     return len(token_ids_a + sep) * [0] + cls_segment_id
   return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str							, lowercase_				:   Optional[str] = None)						->	Tuple[str]:


   """simple docstring"""







   if not os.path.isdir(lowercase_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   _UpperCamelCase           =    os.path.join(
       lowercase_							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

   if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file							, lowercase_)
   elif not os.path.isfile(self.vocab_file):
     with open(lowercase_							, "wb") as fi:
       _UpperCamelCase           =    self.sp_model.serialized_model_proto()
       fi.write(lowercase_)

   return (out_vocab_file,)




